The attitude measurement by the fusing vision and inertial system can utilize the good reproducibility and stability of the visual measurement,and high output frequency and no ambient light interference problem of the inertial measurement.The robustness of the fusion system is weak as statistical properties of system noise and observation noise are not completely known and there may be abnormal measurement values.To solve the poor robustness of the fusion system,a robust quaternion cubature Kalman filter (QCKF) based on diagonalization of matrix is proposed.The influence of robust filter parameters on the measurement accuracy and robustness of the fusion system is analyzed.To improve the stability of numerical calculation,diagonalization of matrix is employed instead of Cholesky decomposition in standard CKF.The proposed method is tested and verified on the vision and inertial measurement system platform.The experimental results show that compared with the standard CKF algorithm,the proposed algorithm has higher accuracy,robustness and stability.%视觉和惯性融合姿态测量系统,可以发挥视觉测量重复性、稳定性好和惯性测量输出频率高、不受环境光干扰的特点.针对融合测量中,系统噪声和观测噪声的统计特性不完全可知及在出现异常测量值时融合测量鲁棒性较差的问题,提出一种基于矩阵对角化变换的鲁棒四元数容积卡尔曼滤波(quaternion cubature Kalman filter,QCKF)算法,分析了鲁棒滤波参数对测量系统鲁棒性和测量准确度的影响,使用矩阵对角化变换代替标准CKF中的Cholesky分解以改善数值计算的稳定性.结合搭建的视觉和惯性融合姿态测量系统平台,实验结果表明与标准CKF算法相比,具有更高的准确度、鲁棒性以及稳定性.
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